tmp work

paddle/framework/op_registry.h

@@ -55,6 +55,10 @@ class OpRegistry {
                          const std::string& grad_op_type) {
     OperatorRegistrar<OpType, ProtoMakerType> reg(op_type.c_str());
     reg.info.grad_op_type_ = grad_op_type;
+    auto proto = reg.info.Proto();
+    std::cout << "====== " << op_type << " =======" << std::endl;
+    std::cout << proto.SerializeAsString() << std::endl;
+    std::cout << "=============" << std::endl;
     ShapeInferenceMap::Instance().CreateOpWithKernel(reg.info, op_type);
     // register gradient op
     if (!grad_op_type.empty()) {

paddle/framework/operator.h

@@ -598,9 +598,9 @@ class OperatorWithKernel : public OperatorBase {
                        });
   }
 
- protected:
   virtual void InferShape(InferShapeContextBase* ctx) const = 0;
 
+ protected:
   // indicate kernel DataType by input data. Defaultly all input data must be
   // same.
   virtual DataType IndicateDataType(const ExecutionContext& ctx) const {

paddle/framework/shape_inference_map.cc

@@ -37,10 +37,13 @@ ShapeInferenceMap& ShapeInferenceMap::Instance() {
 
 void ShapeInferenceMap::CreateOpWithKernel(const OpInfo& op_info,
                                            const std::string& op_type) {
-  const VariableNameMap inputs =
-      ConvertOpProtoVarsToVarNameMap(op_info.Proto().inputs());
+  auto proto = op_info.Proto();
+  std::cout << "========= " << op_type << " in======" << std::endl;
+  std::cout << proto.SerializeAsString() << std::endl;
+  std::cout << "========= " << op_type << " out======" << std::endl;
+  const VariableNameMap inputs = ConvertOpProtoVarsToVarNameMap(proto.inputs());
   const VariableNameMap outputs =
-      ConvertOpProtoVarsToVarNameMap(op_info.Proto().outputs());
+      ConvertOpProtoVarsToVarNameMap(proto.outputs());
   auto* op = op_info.Creator()(op_type, inputs, outputs, {});
   auto* op_with_kernel = dynamic_cast<OperatorWithKernel*>(op);
   auto it = op_shape_inference_map_.find(op_type);

paddle/framework/shape_inference_map.h

@@ -27,14 +27,6 @@ class ShapeInferenceMap {
  public:
   static ShapeInferenceMap& Instance();
 
-  const OperatorBase* GetOperator(const std::string& op_type) {
-    auto it = op_shape_inference_map_.find(op_type);
-    if (it == op_shape_inference_map_.end()) {
-      PADDLE_THROW("op with kernel for Op(%s) is not registered", op_type);
-    }
-    return it->second;
-  }
-
   void CreateOpWithKernel(const OpInfo& op_info, const std::string& op_type);
 
   OperatorWithKernel* GetOpWithKernel(const std::string& op_type) {

paddle/pybind/pybind.cc

@@ -223,6 +223,15 @@ All parameter, weight, gradient are variables in Paddle.
                                    desc.InitializationErrorString());
                     return OpRegistry::CreateOp(desc);
                   })
+      .def("infer_shape",
+           [](const OpDescBind &op_desc, BlockDescBind &block) {
+             auto &shape_inference_map = ShapeInferenceMap::Instance();
+             auto *op = shape_inference_map.GetOpWithKernel(op_desc.Type());
+             if (op != nullptr) {
+               auto ctx = CompileTimeInferShapeContext(op_desc, block);
+               op->InferShape(&ctx);
+             }
+           })
       .def("backward",
            [](const OperatorBase &forwardOp,
               const std::unordered_set<std::string> &no_grad_vars) {

python/paddle/v2/framework/tests/test_infer_shape.py

+import unittest
+import paddle.v2.framework.core as core
+from paddle.v2.framework.op import Operator
+
+
+class TestInferShape(unittest.TestCase):
+    def test_sum_op(self):
+        prog = core.ProgramDesc.__create_program_desc__()
+        self.assertIsNotNone(prog)
+        block = prog.block(0)
+        self.assertIsNotNone(block)
+
+        # prepare input/output
+        x1 = block.new_var("x1")
+        x1.set_shape([10, 20])
+        x2 = block.new_var("x2")
+        x2.set_shape([10, 20])
+
+        out = block.new_var("out")
+
+        # prepare the operator
+        sum_op_desc = block.append_op()
+        sum_op_desc.set_type("sum")
+        sum_op_desc.set_input("X", ["x1", "x2"])
+        sum_op_desc.set_output("Out", ["out"])
+
+        sum_op = Operator("sum", X=["x1", "x2"], Out="out")
+        sum_op.infer_shape(sum_op_desc, block)
+        print(out.shape())